We use a 300-dimenstional embedding layer which is initialized with FastText [1]} based free-trained embeddings for both CNN and the bi-directional LSTM based models. We use a 128-dimensional hidden layer for the bi-directional LSTM and a 32-dimensional filter with kernels of size \(\lbrace 1,3,5,7\rbrace \)  for CNN. For others, we maintain the model settings to resemble the models in [2]}.
We train all of our models for 20 Epochs with a constant batch size of 32. We use early stopping based on the validation set using task-specific metrics (Binary Classification: using roc-auc, Multiclass and question answering based dataset: using accuracy).
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